Reduce receptive field size of CNN while keeping its capacity?
One way to keep the capacity while reducing the receptive field size is to add 1x1 conv layers instead of 3x3 (I did so within the DenseBlocks, there the first layer is a 3x3 conv and now followed by 4 times a 1x1 conv layer instead of the original 3x3 convs (which increase the receptive field)). In doing that, the number of parameters can be kept at a similar level. While 1x1 convolutions are good to add non-linearity, they are not useful to learn spatial information where neighboring pixels/values correlate. This might turn out to be a problem.

Therefore, experiments will have to show if this idea of adding 1x1 conv layers to keep the capacity proves useful or not in learning spatial features on a natural image data set.